{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第一周作业\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练数据和测试数据分割"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "plt.rc('font', family='KaiTi', size=13)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取所有数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "区分测试集和验证集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = data.loc[data['yr']==0]\n",
    "test_data = data.loc[data['yr']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 进行数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>677.402740</td>\n",
       "      <td>2728.358904</td>\n",
       "      <td>3405.761644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105.510663</td>\n",
       "      <td>1.110946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>556.269121</td>\n",
       "      <td>1060.110413</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>1730.000000</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>2915.000000</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>274.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>3632.000000</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3065.000000</td>\n",
       "      <td>4614.000000</td>\n",
       "      <td>6043.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season     yr        mnth     holiday     weekday  \\\n",
       "count  365.000000  365.000000  365.0  365.000000  365.000000  365.000000   \n",
       "mean   183.000000    2.498630    0.0    6.526027    0.027397    3.008219   \n",
       "std    105.510663    1.110946    0.0    3.452584    0.163462    2.006155   \n",
       "min      1.000000    1.000000    0.0    1.000000    0.000000    0.000000   \n",
       "25%     92.000000    2.000000    0.0    4.000000    0.000000    1.000000   \n",
       "50%    183.000000    3.000000    0.0    7.000000    0.000000    3.000000   \n",
       "75%    274.000000    3.000000    0.0   10.000000    0.000000    5.000000   \n",
       "max    365.000000    4.000000    0.0   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     0.684932    1.421918    0.486665    0.466835    0.643665    0.191403   \n",
       "std      0.465181    0.571831    0.189596    0.168836    0.148744    0.076890   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.325000    0.321954    0.538333    0.135583   \n",
       "50%      1.000000    1.000000    0.479167    0.472846    0.647500    0.186900   \n",
       "75%      1.000000    2.000000    0.656667    0.612379    0.742083    0.235075   \n",
       "max      1.000000    3.000000    0.849167    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   365.000000   365.000000   365.000000  \n",
       "mean    677.402740  2728.358904  3405.761644  \n",
       "std     556.269121  1060.110413  1378.753666  \n",
       "min       9.000000   416.000000   431.000000  \n",
       "25%     222.000000  1730.000000  2132.000000  \n",
       "50%     614.000000  2915.000000  3740.000000  \n",
       "75%     871.000000  3632.000000  4586.000000  \n",
       "max    3065.000000  4614.000000  6043.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对体感温度进行单数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b22940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train_data.atemp.values, bins=30, kde=True)\n",
    "plt.xlabel('体感温度', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对风速进行单数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b97208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train_data.windspeed.values, bins=30, kde=True)\n",
    "plt.xlabel('风速', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对天气进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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94Fs0vYN9ksytquer6ukkfwecV1X/neT5JIdW1X1JLgOGe/DzSVPiCGTNekkOA94OrK+qayc59j3A5VX1/Jj2fYGFwIE0k+sta3ctAjYAR1XVliSvBx5st+fR9AquoQmCDVX1otNKUr94mkizWpL9gNXAZcDdSS5L8srtfMtjNKd/SDJnVPv+wFuAAM8BV1XVYVV1QFUdufVDvqq+2wbBiTSh8RRND+Ixg0BdMgw0a7WLvfw28P6qeraq/gv4OjCc5Jw2KMb6R+B9SX4eOHVrY1V9u6r+mmbRk3uBm5NckuRFF4WTvCLJ/sBGmiB4NfAu4LNJPuUSn+qKp4mkMZIcSHOnz/eq6tlx9i8EzqVZPvIPq+rRdtDYMcB+VfX59rgzgHcDVwGfq6rN7emkU2hWyXq4/feHVVVJdqsqF55RJwwDqYfakIingLSrMwwkSV4zkCQZBpIkDANJEoaBJAnDQJIE/D9n/t387hZYZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b1c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_data.weathersit, order=[1,2,3,4]);\n",
    "plt.xlabel('天气类型');\n",
    "plt.ylabel('发生数量');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=train_data.columns \n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 15)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10acee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "plt.savefig('bike_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见温度和体感温度之间，以及温度与租车人数之间有较大相关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fe6d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_data,x_vars='temp',y_vars='atemp')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fe6f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_data,x_vars='atemp',y_vars='cnt')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "去掉一部分特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "removal_set = ['season','yr','mnth','holiday','weekday','workingday','weathersit','atemp','hum','cnt','instant','dteday','casual','registered']\n",
    "removal_set = ['cnt','instant','dteday','casual','registered']\n",
    "removal_set = ['weekday','cnt','instant','dteday','casual','registered']\n",
    "y_train = train_data['cnt'].values\n",
    "X_train = train_data.drop(removal_set, axis = 1)\n",
    "\n",
    "y_test = test_data['cnt'].values\n",
    "X_test = test_data.drop(removal_set, axis = 1)\n",
    "\n",
    "columns = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is -0.7068233792674461\n",
      "The r2 score of RidgeCV on train is 0.7565351434604849\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x532eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.26366508987303555\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#alphas = [ 0.001,0.02,10]\n",
    "n_alphas = 20\n",
    "alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))\n",
    "\n",
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso 回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is -0.7179364180543699\n",
      "The r2 score of LassoCV on train is 0.7563322366228019\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    " \n",
    "lasso = LassoCV()  \n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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